peewit intends to facilitate programming, running and result examination of machine learning experiments. It abstains from setting ML data types, engines, or interfaces, what are to be provided by the user herself or by other libraries, for instance the python-weka-wrapper, https://mloss.org/software/view/548/.

The user is to decompose the experimental code into named nodes that correpsond to aspects or dimension of an experiment. This breakdown gives the machine a grip on the compounds, such that it can support the user in

keeping track of the dimensions in code, stored (intermediate) results and plots

simple parallelization

storing and reintergration of intermediate results.

It also provides some generall houskeeping services like

regime on the file-names and paths, or automatic storing

recovering former experiment versions.

Be aware that the prototype is under development and many features are experimental. Even the underlying experimental model may be modified from time to time.

The core modules depend on python-3 and numpy only but for full func­tion­al­i­ty you fur­ther have to pro­vide uni­son, ssh with agent, git, matplotlib, and graphviz as well as lib­svm for the ex­am­ple project. Feel free to contact us for questions.​